Modeling Unobserved Heterogeneity in Susceptibility to Ambient Benzo[ a ]pyrene Concentration among Children with Allergic Asthma Using an Unsupervised Learning Algorithm

Daniel Fernández, Radim J. Sram, Miroslav Dostal, Anna Pastorkova, Hans Gmuender and Hyunok Choi
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Daniel Fernández: Research and Development Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Dr. Antoni Pujadas, 42, Sant Boi de Llobregat, 08830 Barcelona, Spain
Radim J. Sram: Department of Genetic Ecotoxicology, Institute of Experimental Medicine, Academy of Sciences of the Czech Republic, v.v.i., Vídeňská 1083, 142 20 Prague 4, Czech Republic
Miroslav Dostal: Department of Genetic Ecotoxicology, Institute of Experimental Medicine, Academy of Sciences of the Czech Republic, v.v.i., Vídeňská 1083, 142 20 Prague 4, Czech Republic
Anna Pastorkova: Department of Genetic Ecotoxicology, Institute of Experimental Medicine, Academy of Sciences of the Czech Republic, v.v.i., Vídeňská 1083, 142 20 Prague 4, Czech Republic
Hans Gmuender: Genedata AG, Margarethenstrasse 38, CH-4053 Basel, Switzerland
Hyunok Choi: Departments of Environmental Health Sciences, Epidemiology, and Biostatistics State University of New York at Albany School of Public Health, Rensselaer, NY 12144, USA

IJERPH, 2018, vol. 15, issue 1, 1-18

Abstract: Current studies of gene × air pollution interaction typically seek to identify unknown heritability of common complex illnesses arising from variability in the host’s susceptibility to environmental pollutants of interest. Accordingly, a single component generalized linear models are often used to model the risk posed by an environmental exposure variable of interest in relation to a priori determined DNA variants. However, reducing the phenotypic heterogeneity may further optimize such approach, primarily represented by the modeled DNA variants. Here, we reduce phenotypic heterogeneity of asthma severity, and also identify single nucleotide polymorphisms (SNP) associated with phenotype subgroups. Specifically, we first apply an unsupervised learning algorithm method and a non-parametric regression to find a biclustering structure of children according to their allergy and asthma severity. We then identify a set of SNPs most closely correlated with each sub-group. We subsequently fit a logistic regression model for each group against the healthy controls using benzo[ a ]pyrene (B[ a ]P) as a representative airborne carcinogen. Application of such approach in a case-control data set shows that SNP clustering may help to partly explain heterogeneity in children’s asthma susceptibility in relation to ambient B[ a ]P concentration with greater efficiency.

Keywords: gene-environment interaction; polycyclic aromatic hydrocarbon; asthma; single nucleotide polymorphism; air pollution (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
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